Electroluminescence (EL) imaging has become the standard test procedure for defect detection throughout the production, installation and operation stages of solar modules. Using this test, defects such as micro cracks, broken cells, and finger interruptions on photovoltaic modules could be easily detected and potential power loss issues could be effectively addressed. Although EL test is a very powerful inspection method, interpreting the EL images could be quite challenging due to the inhomogeneous background and complex defect patterns. Therefore, evaluating the damaged cells and determining the defect severity require expertise, and could be time consuming to apply these processes manually for each cell. Hence, the automatic visual inspection of photovoltaic cells is very important. In this study, a novel automatic defect detection and classification framework for solar cells is proposed. In the proposed Deep Feature-Based (DFB) method, the image features extracted through deep neural networks are classified with machine learning methods such as support vector machines, K-Nearest Neighborhood, Decision Tree, Random Forest and Naive Bayes. Thus, classical machine learning and deep learning techniques are used together. In order to combine the features taken from different deep network architectures in various combinations, the minimum Redundancy Maximum Relevance (mRMR) algorithm is employed for the feature selection. In this way, the dimensions of the feature vectors are reduced and the classification performance is increased with fewer features. With the determination of the best features extracted from different layers of deep neural networks, state-of-the-art results were obtained for both 4-class and 2-class datasets. Moreover, a Lightweight Convolutional Neural Network (L-CNN) architecture has been proposed and trained from scratch, and the results are compared with previous works. As a result, the highest scores are obtained using DFB method with Support Vector Machines (SVM) and classification scores of 90.57% and 94.52% were obtained for the dataset with 4 - class and 2 - class, respectively. The proposed DFB-SVM models outperformed other studies using the same dataset. The results showed that the proposed framework can detect PV cell defects with high accuracy.

电致发光（EL）成像已成为在太阳能模块的整个生产，安装和运行阶段进行缺陷检测的标准测试程序。使用此测试，可以轻松检测到诸如微裂纹，电池破裂和光伏模块上的手指中断之类的缺陷，并且可以有效地解决潜在的功率损耗问题。尽管EL测试是一种非常强大的检查方法，但是由于背景不均匀且缺陷图案复杂，因此解释EL图像可能会遇到很大的挑战。因此，评估受损的电池并确定缺陷的严重程度需要专业知识，并且可能需要耗费时间为每个电池手动应用这些过程。因此，光伏电池的自动视觉检查非常重要。在这项研究中，提出了一种新颖的太阳能电池缺陷自动检测与分类框架。在提出的基于深度特征（DFB）的方法中，通过深度神经网络提取的图像特征使用支持向量机，K最近邻，决策树，随机森林和朴素贝叶斯等机器学习方法进行分类。因此，经典机器学习和深度学习技术一起使用。为了将来自不同深度网络体系结构的特征以各种组合进行组合，最小冗余最大相关性（mRMR）算法用于特征选择。以此方式，减少了特征向量的维数，并以更少的特征增加了分类性能。通过确定从深度神经网络的不同层提取的最佳特征，可以获取4类和2类数据集的最新结果。此外，提出了轻量级卷积神经网络（L-CNN）架构并对其进行了训练，并将结果与​​以前的工作进行了比较。结果，使用带有支持向量机（SVM）的DFB方法获得了最高分，对于4类和2类数据集，分别获得了90.57％和94.52％的分类得分。提出的DFB-SVM模型优于使用相同数据集的其他研究。结果表明，所提出的框架可以高精度地检测光伏电池的缺陷。已经提出了轻量级卷积神经网络（L-CNN）架构并对其进行了训练，并将结果与​​以前的工作进行了比较。结果，使用带有支持向量机（SVM）的DFB方法获得了最高分，对于4类和2类数据集，分别获得了90.57％和94.52％的分类得分。提出的DFB-SVM模型优于使用相同数据集的其他研究。结果表明，所提出的框架可以高精度地检测光伏电池的缺陷。已经提出了轻量级卷积神经网络（L-CNN）架构并从头开始对其进行了训练，并将结果与​​以前的工作进行了比较。结果，使用带有支持向量机（SVM）的DFB方法获得了最高分，对于4类和2类数据集，分别获得了90.57％和94.52％的分类得分。提出的DFB-SVM模型优于使用相同数据集的其他研究。结果表明，所提出的框架可以高精度地检测光伏电池的缺陷。对于具有4类和2类的数据集，分别获得52％的数据。提出的DFB-SVM模型优于使用相同数据集的其他研究。结果表明，所提出的框架可以高精度地检测光伏电池的缺陷。对于具有4类和2类的数据集，分别获得52％的数据。提出的DFB-SVM模型优于使用相同数据集的其他研究。结果表明，所提出的框架可以高精度地检测光伏电池的缺陷。